v3.3.0

With the release of v3.3.0, R2R offers a completely RESTful API that covers everything you need for production RAG applications. The biggest change is our Git-like knowledge graph architecture, but we've also unified all the core objects you need to build real applications.

📚 Complete API Coverage:

1️⃣ Content & Knowledge
• Documents: Upload files, manage content, and track extraction status
• Chunks: Access and search vectorized text segments
• Graphs: Git-like knowledge graphs with:
↳ Entities & Relationships
↳ Automatic community detection
↳ Independent graphs per collection

2️⃣ Infrastructure
• Indices: Manage vector indices for search optimization
• Collections: Organize documents and share access
• Users: Built-in auth and permission management
• Conversations: Track chat history and manage branches

3️⃣ Retrieval & Generation
• RAG: Configurable retrieval pipeline with hybrid search
• Agents: Conversational interfaces with knowledge graph integration
• Search: Vector, keyword, and knowledge graph search

💻 Quick Example:

from r2r import R2RClient
client = R2RClient("http://localhost:7272")

# Document level extraction
client.documents.extract(document_id)

# Collection level graph management
client.graphs.pull(collection_id)

# Advanced RAG with everything enabled
response = client.retrieval.rag(
    "Your question here",
    search_settings={
        "use_hybrid_search": True,
        "graph_settings": {"enabled": True}
    }
)

All these components work together seamlessly - just configure what you need and R2R handles the rest. Perfect for teams building serious RAG applications.

🔗 Check the API: https://r2r-docs.sciphi.ai/api-and-sdks/introduction

We'd love feedback from folks building in production!